Training Syllabus

🕘 Duration: 2 full days (8:30 AM – 16:00 PM GMT+7)

👥 Minimum Participants: 5 people

💼 Fee: Based on agreement and venue

📋 Training Overview

The rapid advancement of information technology has compelled organizations to manage and utilize large volumes of data in an effective and integrated manner. Data originating from multiple operational systems are often fragmented, unstructured, and difficult to analyze comprehensively to support strategic decision-making. In this context, data warehouses serve as a critical infrastructure that enables consistent, reliable, and integrated data processing and presentation.

Data warehouse training is essential because the implementation and management of data warehouses require not only technical skills but also a strong conceptual understanding of architecture, data modeling, data quality, as well as data governance and security. Without adequate competencies, organizations risk producing inaccurate, delayed, or irrelevant information, which may ultimately hinder data-driven decision-making. Furthermore, the growing demand for business intelligence, analytics, and digital transformation necessitates human resources capable of designing and managing data warehouses effectively. Therefore, data warehouse training is designed to equip participants with systematic knowledge and practical insights to support analytical needs, enhance the strategic value of data, and strengthen organizational competitiveness. Download Syllabus.

📚 Topics Covered

Session 1 – Introduction to Data Warehouse

  • Fundamental concepts of data warehousing.

  • Differences between data warehouses, operational databases, and data marts.

  • The role of data warehouses in decision-making processes.

  • General data warehouse architecture.

Session 2 – Data Warehouse Architecture and Components

  • Source systems and data integration

  • Staging area

  • Data warehouse storage

  • Metadata and data governance

  • Data warehouse tools and technologies

Session 3 – Data Modeling for Data Warehouse

  • Dimensional modeling concepts.

  • Fact and dimension tables.

  • Star schema.

  • Snowflake schema.

  • Best practices in data warehouse data modeling.

Session 4 – ETL(Extract, Transform, Load) Process

  • ETL concepts and functions

  • Extract: data sources and extraction methods

  • Transform: data cleansing, integration, and standardization

  • Load: data loading strategies

  • Common challenges and pitfalls in ETL processes

Session 5 – Data Quality and Management

  • Data quality concepts (accuracy, completeness, consistency)

  • Data validation and data cleansing

  • Master data management

  • Data governance and data management policies

  • Case study and discussion.

Session 6 – Data Warehouse Security and Access Control

  • Data warehouse security risks.

  • User access control and authorization.

  • Protection of sensitive data.

  • Compliance with data-related regulations.

  • Case Study.

🎯 Learning Outcomes

By the end of this training, participants will be able to:

  • Understand the concepts, architecture, and role of data warehouses in organizations.

  • Explain data modeling and ETL processes in data warehouse environments.

  • Manage data quality, security, and governance in data warehouses.

  • Connect data warehouses with business intelligence and analytics needs.

  • Understand both conceptual and practical aspects of data warehouse implementation.

📞 Contact:

  • 📧 Email: wbudiharto@binus.edu

  • 📱 Bpk. Prof. Widodo (WA): +62 856 9887 384

  • 📱 Ibu Dr. Emny (WA): +62 813 8741 3863

 

Silabus Pelatihan

Data Warehouse

🕘 Durasi: 2 hari fullday (08:30 – 16:00 WIB)

👥 Jumlah Peserta Minimum: 5 orang

💼 Biaya: Sesuai dengan kesepakatan dan lokasi pelatihan

📋 Deskripsi Pelatihan

Perkembangan teknologi informasi yang pesat telah mendorong organisasi untuk mengelola dan memanfaatkan data dalam jumlah besar secara efektif dan terintegrasi. Data yang berasal dari berbagai sistem operasional sering kali tersebar, tidak terstruktur, dan sulit dianalisis secara komprehensif untuk mendukung pengambilan keputusan strategis. Dalam konteks ini, data warehouse berperan sebagai infrastruktur utama yang memungkinkan pengolahan, integrasi, dan penyajian data secara konsisten dan andal.

Pelatihan data warehouse menjadi penting karena implementasi dan pengelolaan data warehouse tidak hanya memerlukan pemahaman teknis, tetapi juga pemahaman konseptual mengenai arsitektur, pemodelan data, kualitas data, serta tata kelola dan keamanan data. Tanpa kompetensi yang memadai, organisasi berisiko menghasilkan informasi yang tidak akurat, terlambat, atau tidak relevan, yang pada akhirnya dapat menghambat pengambilan keputusan berbasis data.  Selain itu, meningkatnya kebutuhan akan business intelligence, analitik, dan transformasi digital menuntut sumber daya manusia yang mampu merancang dan mengelola data warehouse secara efektif. Oleh karena itu, pelatihan data warehouse dirancang untuk membekali peserta dengan pengetahuan dan keterampilan yang sistematis agar mampu mendukung kebutuhan analisis data, meningkatkan nilai strategis data, serta memperkuat daya saing organisasi.

📚 Materi yang Akan Dipelajari

Sesi 1 – Pengantar Data Warehouse

  • Konsep dasar data warehouse.

  • Perbedaan data warehouse, database operasional, dan data mart.

  • Peran data warehouse dalam pengambilan keputusan.

  • Arsitektur data warehouse secara umum.

Sesi 2 – Arsitektur dan Komponen Datawarehouse

  • Source systems dan data integration

  • Staging area

  • Data warehouse storage

  • Metadata dan data governance

  • Tools dan teknologi data warehouse

Sesi 3 – Pemodelan Data untuk Datawarehouse

  • Konsep dimensional modeling.

  • Fakta dan dimensi.

  • Skema bintang (star schema).

  • Skema salju (snowflake schema).

  • Best practices pemodelan data warehouse.

Sesi 4 – Proses ETL  (Extract, Transform, Load)

  • Konsep dan fungsi ETL.

  • Extract: sumber dan metode pengambilan data.

  • Transform: pembersihan, integrasi, dan standardisasi data.

  • Load: strategi pemuatan data.

  • Tantangan dan kesalahan umum dalam ETL.

Sesi 5 – Kualitas dan Manajemen Data

  • Konsep kualitas data (accuracy, completeness, consistency)

  • Data validation dan data cleansing

  • Master data management

  • Data governance dan kebijakan pengelolaan data

Sesi 6 – Kemanan dan Akses Datawarehouse

  • Risiko keamanan data warehouse

  • Kontrol akses dan otorisasi pengguna

  • Perlindungan data sensitif

  • Kepatuhan terhadap regulasi data

🎯 Tujuan Pelatihan

Setelah mengikuti pelatihan ini, peserta diharapkan mampu:

  • Memahami konsep dan ruang lingkup hukum siber Indonesia.

  • Menjelaskan aspek hukum transaksi elektronik dan tanda tangan elektronik.

  • Mengidentifikasi dan menganalisis alat bukti elektronik dalam proses hukum.

  • Memahami prinsip dasar pelindungan data pribadi dan implikasinya bagi pelaku usaha.

  • Meningkatkan kesadaran hukum dan kepatuhan terhadap regulasi siber dalam praktik bisnis dan profesional.

📞 Kontak

  • 📧 Email: wbudiharto@binus.edu

  • 📱 Bpk. Prof. Widodo (WA): +62 856 9887 384

  • 📱 Ibu Dr. Emny (WA): +62 813 8741 3863